Public Disclosure Authorized - World Bank€¦ · 3 . 1. Introduction . By 2030, Africa’s urban population will double, and the difficulties African cities currently face in providing

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Contents

1. Introduction .......................................................................................................................................... 3

2. Objective and Aims ............................................................................................................................... 3

3. Methodology ......................................................................................................................................... 4

1.1 Selection of cities .......................................................................................................................... 4

1.2 Selection of variables and indicators ............................................................................................ 5

1.3 Definition, calculation of intervals and aggregation of indicators ................................................ 7

1.4 Data limitations ............................................................................................................................. 8

2. Results ................................................................................................................................................... 9

4.1 Urbanization challenge ..................................................................................................................... 11

4.2 Solid waste management .................................................................................................................. 11

4.3 Water resources availability ............................................................................................................. 11

4.4 Water supply service ......................................................................................................................... 12

4.5 Sanitation service .............................................................................................................................. 12

4.6 Flood hazard in river basin ................................................................................................................ 13

3. Conclusions ......................................................................................................................................... 13

6. References .............................................................................................................................................. 14

Annex 1 ....................................................................................................................................................... 15

Annex 2 ....................................................................................................................................................... 17

Annex 3 ....................................................................................................................................................... 20

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1. Introduction

By 2030, Africa’s urban population will double, and the difficulties African cities currently face in

providing sustainable water services will be exacerbated. “The Future of Water in African Cities: Why

Waste Water?” (Jacobsen et al. 2012), argues that the traditional approach of one source, one system,

and one discharge cannot close the water gap. A more integrated, sustainable, and flexible approach,

which takes into account new concepts such as water fit to a purpose, is needed in African cities. The

book provides examples of cities in Africa and beyond that have already implemented Integrated Urban

Water Management (IUWM) approaches both in terms of technical and institutional solutions. Case

studies explore the ways in which IUWM can help meet future water demand in African cities. Recent

work carried out by Bahri (2012) on IUWM for the Global Water Partnership has also emphasized the

necessity to examine the challenges posed by urban sprawl for urban planners and to recognize the

need for coordinate, response, and sustainable resource management across sectors, sources, services

and scales.

The World Bank has recognized a need for an integrated approach to urban water management. As part

hereof the issue has come to the forefront: What is the specific character of the water challenge in

African cities and how can we compare the severity of the challenge, the need for integrated approach

and the local capacity to respond to these challenges? The study presented in this companion volume is

an initial attempt to answer this question.

2. Objective and Aims

The objective of this study is to present the results of a comparative analysis of urban water

management for 31 cities in Sub-Saharan Africa. This study is complementary to Jacobsen et al 2012.

The present companion volume presents the methodology and general findings of the comparative

analysis prepared as part of a wider diagnostic of urban water management in 31 cities in Africa.1

Section 3 describes the methodology used to compare the 31 cities according to a selection of variables

and indicators. Section 4 analyses the results of the comparative analysis following 6 individual

categories selected for the study of urban water systems in those 31 cities (urbanization, solid waste

management, water supply service, water resources availability, sanitation service, and flood hazard).

Within each of those categories data have been collected for a number of indicators, see Annex 1. The

result is considered as an index of how each city performs relative to the other 31 cities for that

particular category, see annexes 2 and 3.

There are a number of challenges related to compare challenges and capacities across cities. Availability,

validity and veracity of city level data are issues that may impact on the results. Ideally one would want

city level data, with identical definitions and similarities in data collection verified by the responsible

authorities. For this initial Africa IUWM index, we have relied on publicly available data, from global data

bases to the extent possible as described in Section 3.2. The selection of data, categories and indicators

1 Specific and detailed results for each of the cities is available at http://water.worldbank.org/AfricaIUWM

http://water.worldbank.org/AfricaIUWM

4

has been discussed with a number of experts within the fields of urban planning, environment and

water (see Acknowledgements).

3. Methodology

A number of city level indicators exist. The most ambitious hereof is the Global City Indicators

Previous attempts to categorize and classify cities according to environmental indicators have resulted

in the work produced by the Economist Intelligence Unit and Siemens for cities in Europe, Latin America,

Asia and Africa. (i.e. the green city index used by The Economist (2011) for 15 cities in Africa). The work

by Siemens and The Economist has focused on the categorization and classification of cities according to

their environmental performance and presents an emphasis on the evaluation of each city in

comparison to the rest. The specific regional focus for Africa of this index and its methodology was used

as source and reference for developing the comparative analysis of 31 cities in Africa used by Jacobsen

et al. (2012).

A methodology to characterize the different cities and to compare them was used based on the one

followed by the Economist Intelligence Unit for the African Green City Index (The Economist Intelligence

Unit 2011). This methodology was chosen for its simplicity and also due to the fact that it was able to

limit the level of normalization and aggregation of the indicators by allowing a comparison of the data

indicator by indicator. The choice of this methodology also avoided the ranking of the cities or their

comparison against an established benchmark for it simply compares the values for each indicator for

each city amongst themselves.

1.1 Selection of cities

The 31 cities selected for this comparative study (see Table 1) were chosen based on whether they

fulfilled some or all of the following criteria:

Rate of population growth (more than 3 percent growth rate)2

Size of the cities (more than 2,000,000 inhabitants)3

Presence of World Bank projects

Table 1. List of cities and selection criteria.

No. Country City Population (‘000

Inhabitants)

Population

Growth Rate

1995–2010

Selection

Criteria*

1 Angola Luanda 4,775 5.87 P,G

2 Benin Cotonou 841 2.82 WB

2 According to data from (UNDESA, 2012), World Population Prospects the 2011 Revision.

3 Op. cit.

5

No. Country City Population (‘000

Inhabitants)

Population

Growth Rate

1995–2010

Selection

Criteria*

3 Burkina Faso Ouagadougou 1,324 7.02 WB

4 Cameroon

Douala 2,108 4.56 P,G,WB

5 Yaoundé 1,787 5.45 G,WB

6 Democratic Republic of the Congo

Kinshasa 9,052 4.18 P,G,WB

7 Lubumbashi 1,544 4.06 G,WB

8 Mbuji-Mayi 1,489 4.47 G,WB

9 Republic of Congo Brazzaville 1,505 4.19 G,WB

10 Côte d'Ivoire Abidjan 4,175 3.29 P,G

11 Ethiopia Addis Ababa 3,453 2.06 P,WB

12 Ghana

Accra 2,332 3.27 P,G,WB

13 Kumasi 1,826 5.04 G

14 Guinea Conakry 1,645 3.30 G,WB

15 Kenya Nairobi 3,363 4.08 P,G,WB

16 Malawi

Blantyre 733 N/A WB

17 Lilongwe 866 4.75 G,WB

18 Mozambique Maputo 1,655 1.37 P,WB

19

Nigeria

Lagos 10,572 3.93 P,G,WB

20 Abuja 1,994 8.93 P,G

21 Ibadan 2,835 2.39 P

22 Kano 3,393 2.23 P

23 Senegal Dakar 2,856 3.66 P,G

24

South Africa

Johannesburg 3,618 2.38 P

25 Cape Town 3,357 2.52 P

26 Durban 2,839 2.33 P

27 Sudan Al-Khartum (Khartoum)

5,185 2.53 P

28 Tanzania Dar es Salaam 2,498 4.77 P,G,WB

29 Uganda Kampala 1,597 3.72 G

30 Zambia Lusaka 1,421 4.30 G,WB

31 Zimbabwe Harare 1,663 1.30 WB

Source: Authors.

*Note: Selection criteria: P population size (> 2 million); G growth rate (>3% annual growth); WB World Bank presence.

1.2 Selection of variables and indicators

The selection of variables included in the 31 cities comparative analysis is based on the understanding of

IUWM as a holistic approach to all components of the urban water cycle within the context of the river

basin. The variables chosen for the comparative analysis focus on the aspects of IUWM highlighted by

Jacobsen et al. (2012) and present the main challenges and capacities for IUWM faced by major urban

6

areas in Africa. For the comparative analysis, six different variables were identified that would best

represent the challenges and capacities of IUWM faced by cities in Africa: urbanization challenges, solid

waste management, water resources availability, water supply services, sanitation services and flood

hazards in river basins.4 An internal and multi-disciplinary group of managers and experts at the World

Bank provided important insights and feedback on the selection of indicators.

Out of the 6 variables selected, 16 indicators were identified as being relevant for the comparative

analysis. All of the indicators are based on qualitative data collected from different types of sources.

They aim to measure how each of the cities is faced by certain challenges or capacities associated with

IUWM.5 Data for the 16 indicators was gathered between January and March 2012 as part of a wider

exercise to collect data for a 31 cities diagnostic that informed a World Bank publication (Jacobsen et al.

2012).

Characteristics of the selected indicators:

Representativeness: the first objective with the selection of indicators was to represent and

cover as many aspects of each variable as possible (in terms of completeness, causality, and

complementariness)

Local data: city-level indicators with specific local data were preferred so as to enable

comparison between cities, and to present a more accurate description of the city-level

situation. However, different proxies had to be used in some cases due to data constraints.

Similarly, utility-level data varied depending on the utility’s coverage; mostly, coverage was at

city-level, but some utilities are national (for example, Senegal).

Consistency: indicators are available consistently for all or most of the 31 cities were preferred.

Accessibility: indicators were selected to be accessible and useful to the end-user due to the

target audience being both internal to the World Bank and external (city leaders);

Availability of data: indicator selection process was very much constrained by the availability,

consistency, and reliability of the data for the 31 cities, which highlights the need to systematize

such data for monitoring and planning purposes.

Table 2. Selection of variables and indicators for the comparative analysis of 31 cities in Africa.

Variables Indicator

Urbanization challenge City growth rate, 1995-2010

4 In Jacobsen et al. (2012), the authors include “Economic and institutional strength” as a seventh variable in their

31 city diagnostic exercise. However, it is in this variable were most of the national proxies were used. For this reason, this seventh variable was excluded due to the fact that the main objective of the comparative study is to compare cities with city level data. 5 See Annex for description of indicators and sources. A fully detailed list of sources for each indicator will be

available at http://water.worldbank.org/AfricaIUWM

http://water.worldbank.org/AfricaIUWM

7

Variables Indicator

Percentage of city population living in informal areas

Solid waste management

Percentage of solid waste produced collected (public and private collection)

Percentage of solid waste disposed of in controlled sites

Water resources availability

Average annual runoff

Annual high flow (q10)

Annual low flow (q90)

Groundwater baseflow

Basin yield

Water supply service

Percentage of city population with improved water coverage

Residential water consumption in city or utility coverage area

Percentage of collection rate from population billed

Percentage of revenue water

Sanitation service

Percentage of population with access to improved sanitation

Percentage of wastewater treated

Flood hazard in river basin Frequency of flood events

Note: see annex for the definition and sources for each indicator.

1.3 Definition, calculation of intervals and aggregation of indicators

The data from the selected sub-set of indicators was then homogenized and the mean and standard

deviation for each of the indicators was calculated. . The cities and their corresponding individual values

for every sub-indicator have been assigned to one of 5 intervals depending on how much each of the

individual values differed from the Mean plus or minus x-times the Standard Deviation. Each city value

has been normalized then aggregated into one single indicator, giving equal weight to each of the sub-

indicators. The values have been classified on a scale of 0 to 4 then matched with the interval they

belong to according to their aggregated values.

8

The groups were classified based on different intervals calculated with the Mean score and Standard

Deviation

0= Below Mean minus 1.5 times Standard Deviation

1= Between Mean minus 1.5 Standard Deviation and Mean minus 0.5 times Standard Deviation

2= Between Mean minus 0.5 times Standard Deviation and Mean plus 0.5 times Standard

Deviation

3= Between Mean plus 0.5 times Standard Deviation and Mean plus 1.5 times Standard

Deviation

4= Above Mean plus 1.5 times Standard Deviation

Table 3. Calculation, definition and codification of intervals

Calculation of

intervals

Below Mean -

1.5xSD

Between Mean -

0.5xSD and Mean -

1.5xSD

Between Mean -

0.5xSD and Mean +

0.5xSD

Between Mean

+0.5xSD and

Mean +1.5xSD

Above Mean

+1.5xSD

Codification for

normalization of

intervals

0 1 2 3 4

Values for

intervals

Between 0 and

0.99 Between 1 and 1.99 2

Between 2.01 and

2.99 Between 3 and 4

Definition of

intervals

Well below

average Below average Average Above average

Well above

average

Note: SD = Standard Deviation.

1.4 Data limitations

Obtaining consistent and substantial data to effectuate the comparative analysis proved challenging due

to the lack of substantial and consistent sources of data for IUWM at the city level. The data used in this

comparative study of 31 cities reflects some general inconsistencies in definitions, measurements, and

data collection methodologies. The inherent complexities of the sector, the difficulties in measuring

institutional arrangements, and the validation of the data found, added limitations to the data set. The

calculation of the mean values and standard deviation required the homogenization of the values for

each indicator which presented some problems due to the fact that values from different sources and

different methodologies as well as from different years had to be treated equally in order to calculate

the values for the mean and standard deviation. The reliability of data and sources also affects the

9

quality of the data used in this study and the different types of analyses that can be derived from the

data. Following is a list of several of the main limitations affecting the data set:

The different methodologies used by the different data sources add uncertainty to the data

set.

Different metrics and different definitions used by the sources add precision problems,

which make the homogenization and integration of the indicators difficult,

The use of different sources for the same indicator and different years adds inconsistencies

and complications when homogenizing and normalizing the data to compare the different

indicators.

In some instances, the data was self-reported, which limited its validity.

2. Results

The results of the comparative study of IUWM for 31 cities in Africa are represented following 6

different variables. Each one of the variables illustrates one dimension associated with IUWM as defined

by Jacobsen et al. (2012). The comparative study presents the different dimensions of IUWM by

establishing different levels of desirability for the results for each variable. These results are based on

the relative position above or below the average for each one of the values for the indicators selected

for the 31 cities (see Annex 1).

According to Table 4. Categorization of variables according to level of desirabilityin the case of the

variables “Urbanization challenge” and “Flood hazard in river basin”, the values for the 31 cities scoring

below average will be more desirable. For the rest of the variables, values for the indicators above the

average will be more desirable. A color gradation has been used to help clarify the degree of desirability

for each variable (Green = more desirable; Red = less desirable). Figure 1 shows the overall position of

the 31 cities of the study according to each one of the 6 variables studied. The columns in the tables

contain the overall position above or below the average for each one of the variables.

Table 4. Categorization of variables according to level of desirability

Variable More desirable Less desirable

Urbanization challenge Below average Above average

Solid waste management Above average Below average

Water resources availability Above average Below average

Water supply service Above average Below average

Sanitation service Above average Below average

Flood hazard in river basin Below average Above average

10

Figure 1. Comparative analysis of IUWM for 31 cities in Africa.

11

4.1 Urbanization challenge

Urbanization growth of African cities presents a challenge for increasingly dense urban areas will require

new planning tools to cope with the future demand of urban services and infrastructure. The results of

the study on 31 cities in Africa show the variability and disparity of urban growth trends across the

region. Overall however, they also corroborate the message that urbanization in Sub-Saharan Africa is

happening, and fast: of the 31 cities, 20 are growing at an annual rate of more than 3 percent. Of these

20 cities, Yaoundé, Kumasi, Luanda and Abuja are way ahead with annual growth rates of more than 5

percent. The effects of this rapid urbanization will heighten the challenges associated with the provision

of urban services experienced by local governments and other public and private stakeholders.

The results of the comparative analysis of the urbanization challenge for the 31 cities also compare the

level of informal settlements in each of the cities. The need for improved access to urban services will

also be challenged by large concentrations of populations living in informal areas and the lack of urban

planning in these settlements. In this sample of 31 cities, 7 of them have more than 70 percent of their

population living in informal areas while the average for all 31 is 54.8 percent. Cities well above average

like Yaoundé or Luanda have respectively 75 and 80 percent of their population living in informal

settlements while Cotonou and Abidjan have the lowest percentage of population living in informal

areas (20 and 14 percent respectively).

4.2 Solid waste management

African cities in the study present varying levels of solid waste collection. The average solid waste

collection rate for the 31 cities of this study is 51.7 percent, with 11 cities in the average category. Cities

in Southern Africa are performing well relatively to the rest, showing rates higher than 70 percent (with

Cape Town with 100 percent collection rate as the highest). The lowest collection rate is for two cities in

Nigeria: Mbuji-Mayi (for which there is no formal collection system) and Kano, with 20 percent

collection rate. For solid waste formally disposed of, the average for the 31 cities is 55 percent.

However, waste collection and disposal figures vary widely across the sample of 31 cities from over 90

percent in the three South African cities of the study to 8 percent in Cotonou or 26 percent in Lusaka.

4.3 Water resources availability

The comparative study of 31 cities includes a series of indicators which portray the level of access to

water resources within the river basin providing information about the hydrologic conditions in each of

the cities’ basins. The results of this comparative study for the variable on water resource availability

within the basin show that the distribution of cities is concentrated around the average values for each

of the 5 indicators used for this variable (20 of 31 cities). 5 cities are well below average in terms of

water resources availability within their basin and no cities are well below average.

Baseline data used for the comparative study highlights that the average basin yield basins within which

the 31 cities are located is 188 million cubic meters per year, which gives an indication of the amount of

water reliably available in those basin in an average year. Based on this indicator, 12 cities are located in

12

basins that have an annual basin yield over the average, with the highest value being Douala (1010

million cubic meters per average year) and the lowest being Khartoum with 2.61 million cubic meters.

This shows the clear limitation of this indicator. Khartoum ranks low, because the basin itself has a low

yield although the Nile river runs across Khartoum, the basin yield value measures the availability of

water generated within the basin, and does not reflect what might be available as a result of rivers

flowing through the basin. Utmost care must be used in interpreting this indicator.

4.4 Water supply service

Results for the comparative analysis in water supply service highlight the variability in levels of water

supply across the sample. Additionally, the need for basic water supply coverage for growing

populations meets the reality of old and non-functioning infrastructures in many cities in Sub-Saharan

Africa. The average individual residential water consumption for the 31 cities of the study is 65.7 liters

per capita per day but for 13 of the 31 cities the daily residential water consumption is lower than 50

liters. As cities grow, future infrastructure projects will have to accommodate expected new water

demands and the extension of service coverage. The average access to improved drinking water for the

population of the 31 cities is 65.6 percent but 13 of the 31 cities in the sample are below the average,

with the lowest coverage in Ibadan, Nigeria with 22 percent of the population with access to improved

drinking water.

Levels of utility service and coverage also vary for the 31 cities. The average collection rate from the

population billed by the water utility in the 31 cities is 42.2 percent. Of the 31 cities, 19 have a collection

rate above the average and 12 are underperforming in comparison to the total sample. The lowest

collection rate levels are to be found in Ibadan, Nigeria (13 percent), Abuja also in Nigeria (20 percent)

and Luanda in Angola (23 percent). The level of revenue water, which is the percentage of water

produced that reaches the costumer, is on average 57.8 percent, with the highest levels for Khartoum

(95 percent) and Dakar (80.5 percent) and the lowest for Abuja (20.11 percent) and Kano (40 percent).

4.5 Sanitation service

In this study, sanitation services are depicted by the indicators of access to improved sanitation and

percentage of wastewater treated. According to the comparative study, improvements in access to

sanitation and wastewater treatment are needed in all of the 31 cities of the study. Access to improved

sanitation averages 53.6 percent in the 31 cities but for 12 cities the levels of access to improved

sanitation are lower. The cities of Brazzaville, Dar es Salaam and Lilongwe have the lowest access to

improved sanitation, well below the average (10.5, 12.6 and 14 percent respectively). Moreover, levels

of 41.7 percent and less of wastewater collected and treated by infrastructure for a sample of 8 cities

amongst 13 (for the rest of the 31 cities data is missing) also illustrate the general environmental

challenge caused by the pollution of water bodies and water supply sources around urban areas.

13

4.6 Flood hazard in river basin

In this study, the level of flood hazard in the river basin for each city is represented by the expected

average number of flood events per 100 years. The calculation is based on a hydrological model of peak-

flow magnitude for annual runoff exceeded by 10 percent for years 1961–1999 (Annual high flow q10,

see Annex 1) and on observed flood events from 1999 to 2007 from the Dartmouth flood observatory

(Jacobsen et al. 2012). Based on that frequency of flood events, 12 of the 31 cities have an average level

of frequency of floods of 9.9 events over 100 years. The results of the comparative study also show that

10 cities are well above the average in terms of expected frequency of flood events, with cities like Kano

or Khartoum with the highest level of expected exposure to flood events (with 25 and 21 expected flood

events respectively over a period of 100 years). Of the 31 cities in the study, 9 cities have levels of

expected flood events below the average, with cities like Johannesburg, Lusaka or Harare with 2

expected flood events and Cape Town, Lagos and Luanda with 7 expected flood events over 100 years.

3. Conclusions

The results of the comparative analysis of the different variables related with urban water management

show varying levels of development for each of the 31 cities. Based on this comparative analysis and

although general trends are difficult to establish and regional tendencies have to be taken into account,

there is large room for improvement for all cities and for most of the indicators.

The results of the comparative analysis of 31 cities in Africa also show that there is no clear “winner”.

Although some cities, mainly in southern Africa, perform better on average than some of the other

cities, they also have challenges of their own that cannot be overestimated. The need to plan the city of

the future while acknowledging and accounting for the urban challenges of today demonstrates that an

effort is needed to understand the necessities and also capacities faced by urban areas in terms of

increasing risks linked to population growth or climate change that will affect access and delivery of

services, sustainable development of resources.

The development of this type of comparative study can be useful, provided that there is general and

available data to be used. The preliminary work carried out for this report highlighted problems with

data collection that need to be addressed if a wider and more detailed comparative exercise is to be

undertaken. An emphasis on good quality city-level data needs to be pursued. Most often, data about

urban water management generated at the local level is incomplete or inconsistent which presents

problems for any type of comparative analysis. Sound management and planning decisions are based on

rigorous information. The gaps and limitations of the data used in the study of 31 cities and the lack of

consistent monitoring tools represent a serious hindrance to the understanding of current and future

needs for basic urban services in Sub-Saharan Africa.

Additionally, the analysis and comparison of the institutional aspects of urban water management

proved difficult based on the lack of specific and up-to-date information about local policies and

institutions. The task at hand should be to gather information in order to compare at a local level the

14

effects of policies and institutions on the provision of urban services and the management of integrated

urban water systems.

6. References

Bahri, A. (2012) Integrated Urban Water Management, TEC Background Papers, No.16, Global Water

Partnership.

Jacobsen, M. et al. (2012) The future of water in African Cities: why waste water? , Water Paper, The

World Bank.

The Economist Intelligence Unit (2011) African Green City Index: Assessing the environmental performance of Africa’s Major Cities, Siemens: Germany.

15

Annex 1

Table 1. Selection of variables and indicators for the comparative analysis of 31 cities in Africa.

Variables Indicator Type Units Weight Notes and sources

Urbanization challenge

City growth rate, 1995-2010

Quantitative % 50% UNDESA, 2012

Percentage of city population living in informal areas

Quantitative % 50% Various sources

Solid waste management

Percentage of solid waste produced collected (public and private collection)


Percentage of solid waste disposed of in controlled sites



Average annual runoff

Quantitative Million cubic meters (MCM)/year

20% World Bank Data, Climate Change Knowledge Portal. For detailed methodology see Strzepek, et al., 2011. Average modeled runoff at basin scale for years 1961–1999.

Annual high flow (q10)

Quantitative MCM/year 20%

Annual runoff exceeded by 10 percent of the time for years 1961–1999. Source: World Bank Data, Climate Change Knowledge Portal. For detailed methodology see Strzepek et al., 2011.

Annual low flow (q90)

Quantitative MCM/year 20%

Annual runoff exceeded 90 percent of the time for years 1961–1999. Source: World Bank Data, Climate Change Knowledge Portal. For detailed methodology see Strzepek et al., 2011.

Groundwater baseflow

Quantitative MCM/year 20% Sustained flow in a river resulting from groundwater. Source: World Bank Data, Climate Change Knowledge Portal. For detailed methodology see Strzepek et al., 2011.

Basin yield Quantitative MCM/year 20% World Bank Data, Climate Change Knowledge Portal. For detailed methodology see Strzepek et al., 2011. Maximum sustainable reservoir releases within the basin for years 1961–1999

16

Variables Indicator Type Units Weight Notes and sources

Water supply service

Percentage of city population with improved water coverage

Quantitative % 25% Improved water coverage as per source’s definition. Various sources

Residential water consumption in city or utility coverage area

Quantitative l/cap/day 25% Total residential water consumption, in liters per capita per day. Relates to population served by utility or population living in city, depending on the source. Various sources

Percentage of collection rate from population billed


Percentage of revenue water

Quantitative % 25% Percentage of water produced that reaches the costumer. Calculated based on the percentage of nonrevenue water, which is the percentage of water produced and lost before reaching the customer, either through leaks, theft, or legal use for which no payment is made. Various sources.

Sanitation service

Percentage of population with access to improved sanitation


Percentage of wastewater treated

Quantitative % 50% Percentage of wastewater treated by treatment plant system of percentage of wastewater collected. Various sources

Flood hazard in river basin

Frequency of flood events

Quantitative Number of events/100 years

100% Estimate of flood frequency as the expected average number of events per 100 years (hydrological model of peak-flow magnitude). Sources: UNEP/GRID-Europe PREVIEW flood data set, Strzepek et al., 2011; Dartmouth Flood Observatory, Dartmouth College.

17

Annex 2

This annex shows the individual tables for each of the 6 variables of the comparative study.

Table 1. Urbanization challenge.

Well below average Below average Average Above average

Well above average

Harare Maputo Ibadan Lusaka Yaounde Kano Douala Lagos Kumasi Johannesburg Addis Ababa Ouagadougou Luanda Cape Town Accra Kinshasa Abuja Durban Conakry Lilongwe Dar es Salaam Khartoum Nairobi Cotonou Kampala Abidjan Lubumbashi Dakar Mbuji-Mayi Blantyre Brazzaville

Table 2. Solid waste management.

Well above average

Above average Average Below average Well below

average Maputo Nairobi Ibadan Kano Mbuji-Mayi

Johannesburg Khartoum Cotonou Cape Town Lusaka Blantyre

Durban Harare Douala Yaoundé

Conakry Abidjan Kumasi Lagos Kampala Ouagadougou Dar es Salaam

No data Addis Ababa, Accra, Dakar, Luanda, Kinshasa, Lubumbashi, Brazzaville, Lilongwe, Abuja

More desirable

Less desirable

More desirable

Less desirable

18

Table 3. Water resources availability.

Well above average Above average Average Below average

Well below average

Douala Addis Ababa Maputo Johannesburg Yaounde Kumasi Ibadan Khartoum Conakry Lubumbashi Kano Luanda Kinshasa Abuja Cape Town

Brazzaville Durban Lusaka Harare Cotonou Abidjan Accra Nairobi Lagos Dakar Kampala Ouagadougou Mbuji-Mayi Blantyre Lilongwe Dar es Salaam

Table 4. Water supply service.

Well above average

Above average Average Below average Well below

average Cape Town Johannesburg Lusaka Maputo

Abidjan Durban Harare Ibadan Dakar Khartoum Yaounde Kano

Cotonou Lagos Douala Addis Ababa Kinshasa Accra Kumasi Mbuji-Mayi Luanda Conakry Brazzaville Nairobi Abuja Kampala Ouagadougou Lubumbashi

More desirable

Less desirable

More desirable

Less desirable

19

Blantyre Lilongwe Dar es Salaam

Table 5. Sanitation service.

Well above average

Above average

Average Below average Well below

average Johannesburg Maputo Ibadan Cotonou

Cape Town Dakar Kano Luanda Durban Dar es Salaam Khartoum Ouagadougou Kumasi Lusaka Lubumbashi Nairobi Harare Brazzaville Lagos Douala Blantyre

Kampala Yaounde Lilongwe Abidjan Addis Ababa Accra Conakry Kinshasa

No data Mbuji-Mayi

Table 6. Flood hazard in river basin.

Well below average

Below average Average Above

average Well above average

Johannesburg Maputo Kano Lusaka Ibadan Khartoum Harare Cape Town Cotonou Douala Durban Nairobi Yaounde Addis Ababa Kampala Abidjan Conakry Ouagadougou

Accra Lagos Lubumbashi Kumasi Dakar Blantyre Mbuji-Mayi Luanda Lilongwe Kinshasa Abuja Brazzaville Dar es Salaam

More desirable

Less desirable

More desirable

Less desirable

20

Annex 3

This annex shows individual tables for each of the 31 cities.

Table 1. Luanda.

Urbanization Challenge Well

above average

Solid waste management system


Below

average

Water supply service Below

average

Sanitation service Below

average


Average

Table 2. Cotonou.

Urbanization Challenge Below

average


Below

average


Average

Water supply service Above

average


average


Well

above average

21

Table 3. Ouagadougou.

Urbanization Challenge Above

average


Average


Average


average


average


Well

above average

Table 4. Douala.

Urbanization Challenge Average


Well above average


Well above average


average

Sanitation service Average


Below

average

22

Table 5. Yaoundé


above average


Average


Well above average

Water supply service Average



Below

average

Table 6. Brazzaville.




Well above average


average


average


Average

23

Table 7. Kinshasa.


average



Well above average




Average

Table 8. Lubumbashi.




Above

average


average


average


Well

above average

24

Table 9. Mbuji-Mayi.



Well

below average


Average


Sanitation service


Below

average

Table 10. Abidjan.


average


Average


Average

Water supply service Well above

average



Below

average

25

Table 11. Addis Ababa.




Above

average


average



Average

Table 12. Accra.




Average


average



Below

average

26

Table 13. Kumasi.


above average


Average


Above

average


average

Sanitation service Well above

average


Below

average

Table 14. Conakry.



Average


Above

average


average



Average

27

Table 15. Nairobi.



Above

average


Average


average


average


Well

above average

Table 16. Blantyre.


average


Below

average


Average


average


average


Wel above

average

28

Table 17. Lilongwe.


average



Average


average


average


Well

above average

Table 18. Maputo


average


Above

average


Average


average

Sanitation service Above

average


Average

29

Table 19. Abuja.


above average



Above

average


average



Well

above average

Table 20. Ibadan



Average


Average


average



Average

30

Table 21. Kano.


average


Below

average


Average


average



Above

average

Table 22. Lagos.


average


Average


Average



average


Average

31

Table 23. Dakar.


average



Average


average


average


Average

Table 24. Cape Town.


average


Well above average


Average


average


average


Average

32

Table 25. Durban.


average


Well above average


Average


average


average


Average

Table 26. Johannesburg.


average


Well above average


Below

average


average


average


Below

average

33

Table 27. Khartoum.


average


Average


Below

average


average



Well

above average

Table 28. Dar es Salaam.


above average


Average


Average


average


average


Average

34

Table 29. Kampala.



Average


Average


average


average


Well

above average

Table 30. Lusaka.


average


Average


Average




Below

average

35

Table 31. Harare.

Urbanization Challenge Well below

average


Average


Average




Below

average

Documents

Public Disclosure Authorized - World Bank€¦ · 3 . 1. Introduction . By 2030, Africa’s urban population will double, and the difficulties African cities currently face in providing